ABSTRACT
This chapter shows that different model types can be used in an Model Predictive Control (MPC) framework. It gives a brief summary of common linear models. The Controlled auto-regressive integrated moving average model allows systematic inclusion of a disturbance models which therefore facilitates affective disturbance rejection. Independent models are not a different form of model; however, it is important to include a short discussion in this chapter because there is a key difference in the philosophy of how the model is used. Although state-space models are usually favoured for the multi-input/multi-output case, an observer is still required which is not the case with transfer function models. The implication for modelling is that in the identification stage one can afford to underestimate the dead time and let the identification algorithm insert small values against coefficients that perhaps should be zero.
